The best way to stay in touch with PuneTech and associated activities is to subscribe and receive most recent updates in your email inbox or via RSS. And, if you are looking for special interest groups, Click here. See our About Page to find out more about what PuneTech is.

And this is not confined to national boundaries. It is one of only two (as far as I know) Pune-based companies to be featured in TechCrunch (actually TechCrunchIT), one of the most influential tech blogs in the world (the other Pune company featured in TechCrunch is Pubmatic).

Why all this attention for Druvaa? Other than the fact that it has a very strong team that is executing quite well, I think two things stand out:

It is one of the few Indian product startups that are targeting the enterprise market. This is a very difficult market to break into, both, because of the risk averse nature of the customers, and the very long sales cycles.

Druvaa has two main products. Druvaa inSync allows enterprise desktop and laptop PCs to be backed up to a central server with over 90% savings in bandwidth and disk storage utilization. Druvaa Replicator allows replication of data from a production server to a secondary server near-synchronously and non-disruptively.

We now dig deeper into each of these products to give you a feel for the complex technology that goes into them. If you are not really interested in the technology, skip to the end of the article and come back tomorrow when we’ll be back to talking about google keyword searches and web-2.0 and other such things.

Druvaa Replicator

Overall schematic set-up for Druvaa Replicator

This is Druvaa’s first product, and is a good example of how something that seems simple to you and me can become insanely complicated when the customer is an enterprise. The problem seems rather simple: imagine an enterprise server that needs to be on, serving customer requests, all the time. If this server crashes for some reason, there needs to be a standby server that can immediately take over. This is the easy part. The problem is that the standby server needs to have a copy of the all the latest data, so that no data is lost (or at least very little data is lost). To do this, the replication software continuously copies all the latest updates of the data from the disks on the primary server side to the disks on the standby server side.

This is much harder than it seems. A simple implementation would simply ensure that every write of data that is done on the primary is also done on the standby storage at the same time (synchronously). This is unacceptable because each write would take unacceptably long and this would slow down the primary server too much.

If you are not doing synchronous updates, you need to start worrying about write order fidelity.

Write-order fidelity and file-system consistency

If a database writes a number of pages to the disk on your primary server, and if you have software that is replicating all these writes to a disk on a stand-by server, it is very important that the writes should be done on the stand-by in the same order in which they were done at the primary servers. This section explains why this is important, and also why doing this is difficult. If you know about this stuff already (database and file-system guys) or if you just don’t care about the technical details, skip to the next section.

Imagine a bank database. Account balances are stored as records in the database, which are ultimately stored on the disk. Imagine that I transfer Rs. 50,000 from Basant’s account to Navin’s account. Suppose Basant’s account had Rs. 3,00,000 before the transaction and Navin’s account had Rs. 1,00,000. So, during this transaction, the database software will end up doing two different writes to the disk:

write #1: Update Basant’s bank balance to 2,50,000

write #2: Update Navin’s bank balance to 1,50,000

Let us assume that Basant and Navin’s bank balances are stored on different locations on the disk (i.e. on different pages). This means that the above will be two different writes. If there is a power failure, after write #1, but before write #2, then the bank will have reduced Basant’s balance without increasing Navin’s balance. This is unacceptable. When the database server restarts when power is restored, it will have lost Rs. 50,000.

After write #1, the database (and the file-system) is said to be in an inconsistent state. After write #2, consistency is restored.

It is always possible that at the time of a power failure, a database might be inconsistent. This cannot be prevented, but it can be cured. For this, databases typically do something called write-ahead-logging. In this, the database first writes a “log entry” indicating what updates it is going to do as part of the current transaction. And only after the log entry is written does it do the actual updates. Now the sequence of updates is this:

Now if the power failure occurs between writes #0 and #1 or between #1 and #2, then the database has enough information to fix things later. When it restarts, before the database becomes active, it first reads the logging section of the disk and goes and checks whether all the updates that where claimed in the logs have actually happened. In this case, after reading the log entry, it needs to check whether Basant’s balance is actually 2,50,000 and Navin’s balance is actually 1,50,000. If they are not, the database is inconsisstent, but it has enough information to restore consistency. The recovery procedure consists of simply going ahead and making those updates. After these updates, the database can continue with regular operations.

(Note: This is a huge simplification of what really happens, and has some inaccuracies – the intention here is to give you a feel for what is going on, not a course lecture on database theory. Database people, please don’t write to me about the errors in the above – I already know; I have a Ph.D. in this area.)

Note that in the above scheme the order in which writes happen is very important. Specifically, write #0 must happen before #1 and #2. If for some reason write #1 happens before write #0 we can lose money again. Just imagine a power failure after write #1 but before write #0. On the other hand, it doesn’t really matter whether write #1 happens before write #2 or the other way around. The mathematically inclined will notice that this is a partial order.

Now if there is replication software that is replicating all the writes from the primary to the secondary, it needs to ensure that the writes happen in the same order. Otherwise the database on the stand-by server will be inconsistent, and can result in problems if suddenly the stand-by needs to take over as the main database. (Strictly speaking, we just need to ensure that the partial order is respected. So we can do the writes in this order: #0, #2, #1 and things will be fine. But #2, #0, #1 could lead to an inconsistent database.)

Replication software that ensures this is said to maintain write order fidelity. A large enterprise that runs mission critical databases (and other similar software) will not accept any replication software that does not maintain write order fidelity.

Why is write-order fidelity difficult?

I can here you muttering, “Ok, fine! Do the writes in the same order. Got it. What’s the big deal?” Turns out that maintaining write-order fidelity is easier said than done. Imagine the your database server has multiple CPUs. The different writes are being done by different CPUs. And the different CPUs have different clocks, so that the timestamps used by them are not nessarily in sync. Multiple CPUs is now the default in server class machines. Further imagine that the “logging section” of the database is actually stored on a different disk. For reasons beyond the scope of this article, this is the recommended practice. So, the situation is that different CPUs are writing to different disks, and the poor replication software has to figure out what order this was done in. It gets even worse when you realize that the disks are not simple disks, but complex disk arrays that have a whole lot of intelligence of their own (and hence might not write in the order you specified), and that there is a volume manager layer on the disk (which can be doing striping and RAID and other fancy tricks) and a file-system layer on top of the volume manager layer that is doing buffering of the writes, and you begin to get an idea of why this is not easy.

Naive solutions to this problem, like using locks to serialize the writes, result in unacceptable degradation of performance.

Druvaa Replicator has patent-pending technology in this area, where they are able to automatically figure out the partial order of the writes made at the primary, without significantly increasing the overheads. In this article, I’ve just focused on one aspect of Druvaa Replicator, just to give an idea of why this is so difficult to build. To get a more complete picture of the technology in it, see this white paper.

Druvaa inSync

Druvaa inSync is a solution that allows desktops/laptops in an enterprise to be backed up to a central server. (The central server is also in the enterprise; imagine the central server being in the head office, and the desktops/laptops spread out over a number of satellite offices across the country.) The key features of inSync are:

The amount of data being sent from the laptop to the backup server is greatly reduced (often by over 90%) compared to standard backup solutions. This results in much faster backups and lower consumption of expensive WAN bandwidth.

It stores all copies of the data, and hence allows timeline based recovery. You can recover any version of any document as it existed at any point of time in the past. Imagine you plugged in your friend’s USB drive at 2:30pm, and that resulted in a virus that totally screwed up your system. Simply uses inSync to restore your system to the state that existed at 2:29pm and you are done. This is possible because Druvaa backs up your data continuously and automatically. This is far better than having to restore from last night’s backup and losing all data from this morning.

It intelligently senses the kind of network connection that exists between the laptop and the backup server, and will correspondingly throttle its own usage of the network (possibly based on customer policies) to ensure that it does not interfere with the customer’s YouTube video browsing habits.

Let’s dig a little deeper into the claim of 90% reduction of data transfer. The basic technology behind this is called data de-duplication. Imagine an enterprise with 10 employees. All their laptops have been backed up to a single central server. At this point, data de-duplication software can realize that there is a lot of data that has been duplicated across the different backups. i.e. the 10 different backups of contain a lot of files that are common. Most of the files in the C:\WINDOWS directory. All those large powerpoint documents that got mail-forwarded around the office. In such cases, the de-duplication software can save diskspace by keeping just one copy of the file and deleting all the other copies. In place of the deleted copies, it can store a shortcut indicating that if this user tries to restore this file, it should be fetched from the other backup and then restored.

Data de-duplication doesn’t have to be at the level of whole files. Imagine a long and complex document you created and sent to your boss. Your boss simply changed the first three lines and saved it into a document with a different name. These files have different names, and different contents, but most of the data (other than the first few lines) is the same. De-duplication software can detect such copies of the data too, and are smart enough to store only one copy of this document in the first backup, and just the differences in the second backup.

The way to detect duplicates is through a mechanism called document fingerprinting. Each document is broken up into smaller chunks. (How do determine what constitutes one chunk is an advanced topic beyond the scope of this article.) Now, a short “fingerprint” is created for each chunk. A fingerprint is a short string (e.g. 16 bytes) that is uniquely determined by the contents of the entire chunk. The computation of a fingerprint is done in such a way that if even a single byte of the chunk is changed, the fingerprint changes. (It’s something like a checksum, but a little more complicated to ensure that two different chunks cannot accidently have the same checksum.)

All the fingerprints of all the chunks are then stored in a database. Now everytime a new document is encountered, it is broken up into chunks, fingerprints computed and these fingerprints are looked up in the database of fingerprints. If a fingerprint is found in the database, then we know that this particular chunk already exists somewhere in one of the backups, and the database will tell us the location of the chunk. Now this chunk in the new file can be replaced by a shortcut to the old chunk. Rinse. Repeat. And we get 90% savings of disk space. The interested reader is encouraged to google Rabin fingerprinting, shingling, Rsync for hours of fascinating algorithms in this area. Before you know it, you’ll be trying to figure out how to use these techniques to find who is plagiarising your blog content on the internet.

Back to Druvaa inSync. inSync does fingerprinting at the laptop itself, before the data is sent to the central server. So, it is able to detect duplicate content before it gets sent over the slow and expensive net connection and consumes time and bandwidth. This is in contrast to most other systems that do de-duplication as a post-processing step at the server. At a Fortune 500 customer site, inSync was able reduce the backup time from 30 minutes to 4 minutes, and the disk space required on the server went down from 7TB to 680GB. (source.)

Again, this was just one example used to give an idea of the complexities involved in building inSync. For more information on other distinguishinging features, check out the inSync product overview page.

Have questions about the technology, or about Druvaa in general? Ask them in the comments section below (or email me). I’m sure Milind/Jaspreet will be happy to answer them.

Also, this long, tech-heavy article was an experiment. Did you like it? Was it too long? Too technical? Do you want more articles like this, or less? Please let me know.